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@article{151650,
        author = {Asawari Pensalwar and Mallisha Patkar and Rachana Dhannawat},
        title = {Training CNN To Categorize Pulmonary Radiological Imaging},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {1},
        pages = {455-461},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151650},
        abstract = {Covid-19 is a severe acute respiratory syndrome that can be spread by close contact with an infected person. In this paper, we have proposed two convolutional neural network architectures to classify the image and see if the patient has COVID-19 or not. The first proposed architecture is using dropout layer and the other is without the dropout layer. The models are tested for two datasets: the original dataset and the dataset with augmentation. After testing, we got the highest accuracy of 98% with model 4 i.e. without dropout layer and with no data augmentation. We combined this model with webpage which can be used by normal people to know whether they are suffering from COVID-19 or not with probability of its occurrence.},
        keywords = {CNN, Chest x-ray,covid-19 Detection},
        month = {},
        }